The univariate breeder's Self-care for anxiety is a useful heuristic Metxbolic for understanding how microevolutionary processes see Glossary work. Glazier, b. J Exp Biol.

Metabolic rate and genetics -

A test with 50 mice using both step increments showed that MMR was slightly higher 2. Within any generation, all measurements were made using the same step increments. We used an instantaneous correction for chamber washout to determine MMR Bartholomew et al.

Basal metabolic rate was measured at least 2 days after the MMR trial. Water and CO 2 were removed from the excurrent air with a column containing Drierite and Ascarite II. LabVIEW 7. BMR was estimated as the lowest 5-min steady-state rate of O 2 consumption from the six min measurement periods for each mouse using equation 4 from Hill , p.

Before phenotypic and genetic trend analyses, we screened the data from each generation for outliers using least squares multiple regression of BMR or MMR on body mass and other covariates age, treadmill, observer that is, person who conducted the treadmill test and BMR chamber number.

In addition, treatment and sex were fitted as fixed effects, and line nested in treatment and replicate were each fitted as random effects. Statistical analyses were performed using SAS, v. If standardized residuals from these regressions were greater than absolute value 3.

Most BMR outliers were positive suggesting that the mice had not been quiescent enough to be considered at their BMR. Similarly, most MMR outliers were negative, which likely resulted from mice that did not reach their MMR due to submaximal running effort or observer error for example, because observer decisions about precisely when to end a trial might have influenced the estimated MMR.

Significant covariates were included in the models as fixed effects. For phenotypic trend analyses, we ran separate mixed models for each generation where treatment and sex were fitted as fixed effects. In addition, line nested in treatment and replicate were fitted as two random effects.

For the phenotypic analyses of BMR, body mass, age and chamber CHAM in which the animal was measured also were included as fixed effects. For the phenotypic MMR analyses, body mass, age, treadmill TRED and observer OBS were included as fixed effects.

Treadmill was included because there was a significant difference between the two motorized treadmills that we think was caused by differences in the stimulators used through G 3. After G 3 , we used identical custom-built stimulators to eliminate this design flaw. To determine the differences between treatments, we used post hoc contrasts.

These contrasts were control versus antag-MR, control versus high-MMR and antag-MR versus high-MMR. Tukey-adjusted P -values were calculated to determine significance of post hoc pairwise comparisons.

In a phenotypic analysis, which is carried out within generations, an estimate of genetic change is obtained by studying the difference between corrected phenotypic means of a particular selection treatment and the corrected phenotypic means of the control line.

This approach does not make strong assumptions about the genetic architecture of the trait analyzed, but results in sampling variances of estimates of response that are considerably larger than those from a mixed-model analysis. In other words, it produces a more erratic picture of the evolution of genetic means.

A mixed-model approach extracts more information from the data than the least squares alternative, at the cost of making stronger assumptions about the genetic mechanism operating Sorensen and Kennedy, The results reported below indicate that similar qualitative conclusions are drawn from both methods of inference.

The Bayesian analysis reported below is based on the method described in Sorensen et al. Essentially, it consists of computing the posterior distribution of the average genetic values across individuals over generations, accounting for all other sources of variation included in the model.

In the Bayesian analysis, variance components and genetic trend are computed simultaneously. We have also carried out a restricted maximum likelihood REML analysis of variance and covariance components and confirmed that inferences were in excellent agreement. Results from the REML analysis can be found in the Supplementary Tables A4, A5.

The data consists of records of BMR and records of MMR. Note that for generations 2—4, we did not collect BMR data from the high-MMR lines in a manner that would allow us to estimate the correlated response of BMR to selection on MMR. The complete pedigree included individuals.

Three sets of analyses were undertaken. First, the data were analyzed separately for each of the 12 lines 12 analyses using model [1]. Second, the data were analyzed separately for each treatment three analyses using model [2]. Third, a single analysis of the 12 lines three treatments, four replicates per treatment was conducted using model [2].

where SEX, CHAM, TRED and OBS are fixed effect classes; mass BMR, age BMR, mass MMR and age MMR are fixed regressions; CAGE natal cage , REPLICATE × GEN and ANIM that is, additive genetic effect are random effects; and e is the random residual.

S is the additive relationship matrix of dimension equal to number of individuals in the pedigree, A represents additive genetic effect, L represents replicates, C represents cage and G represents generations.

I C is an identity matrix of dimension equal to number of cages for the model across line and treatment, this is equal to , I LG is an identity matrix of dimension equal to number of replicates × generations equal to 36 and I e is an identity matrix of dimension equal to the number of observations in the particular analysis where:.

In the above expressions, subscript B represents BMR, and M represents MMR. In and are the additive genetic variance components for BMR and for MMR, respectively, and is the additive genetic covariance between the traits.

The structure of the residual covariance matrix R is complicated due to the pattern of missing data. R is a function of the residual variance for MMR, , the residual variance for BMR, , and the residual covariance between both traits,.

For each analysis, a pedigree file was extracted from the total pedigree file, so the pedigree used in each analysis only included individuals with data for the specific analysis and their ancestors back to generation 0. The number of observations and number of animals in the pedigree for each of the 16 analyses are shown in Table 1.

Each analysis was carried out using both Bayesian and REML methods with the DMU-package Madsen and Jensen, While REML analyses retrieve inferences about dispersion parameters, Bayesian analyses generate both dispersion parameters and genetic trends.

The Bayesian linear mixed models were implemented with a standard Gibbs sampling algorithm see, for example, Sorensen and Gianola, This chain length was adequate as judged by the computation of Monte Carlo variances and effective chain sizes for all dispersion parameters, which were obtained by the method of batching see Sorensen and Gianola, Convergence behavior of the Gibbs chains was checked by the inspection of trace plots.

The Gibbs chains showed good mixing behavior. Body mass, whole-animal BMR and MMR were obtained for over laboratory mice with a complete pedigree Supplementary Table A1. Mean mass-adjusted MMR that is, MMR adjusted for differences in body mass and other factors, hence mass-independent MMR were very similar in the initial generations across treatments Table 2.

After eight generations of selection, mass-adjusted MMR had increased in all the three treatments, including the controls, which provides an example of why control lines are needed in selection experiments. Mass-adjusted MMR was lowest in the control mice, intermediate in the antagonistically selected mice and highest in the directionally selected mice.

There was no significant difference in body mass at time of MMR measurements among control, antag-MR and high-MMR treatments. Mean mass-adjusted BMRs were similar in the starting generations across treatments Table 2. After eight generations of selection, mass-adjusted BMR had decreased in all the three treatments.

Recall that we expected a decrease in BMR due to selection for decreased BMR in the antag-MR mice and that we expected an increase in BMR as a correlated response to selection for increased MMR in the high-MMR mice.

Consistent with these expectations, mass-adjusted BMR was lowest in antag-MR, intermediate in the controls and highest in the high-MMR mice. There was no significant difference in body mass at the time of BMR measurements among control, antag-MR and high-MMR treatments.

For the three sets of the Bayesian analyses, genetic means per generation that is, genetic trend for MMR and BMR were estimated as the average of the posterior means of the additive genetic effects ANIM in the model 1 for MMR and BMR, respectively, for each combination of treatment, replicate and generation.

In the lines selected for MMR, the genetic trend for MMR represents the direct response to selection for MMR, and the genetic trend for BMR represents the correlated response in BMR. In the lines selected for antag-MR, the genetic trends for MMR and for BMR represent both metabolic rates responses to selection Figure 1.

Genetic trends from Bayesian analyses for basal metabolic rate BMR and maximal metabolic rate MMR. In all the figures, the y axis displays the average genetic means that is, estimated breeding values—EBV , and the x axis displays generation numbers.

Posterior means of genetic means per generation were calculated together with posterior standard deviations. a Represents the results of the 12 analyses within treatments and replicates. Trends are plotted for each treatment and for each replicate. b Represents the results from the three analyses within treatments only , but these figures show estimates of trends for each treatment and replicate.

c The single joint analysis and the results are shown for each treatment and replicate. TRT, treatment; High-MMR, directional selection for increased mass-independent MMR; Antag-MR, antagonistic selection for increased mass-independent MMR and decreased mass-independent BMR.

Blue triangle—BMR, red diamond—MMR. A full color version of this figure is available at the Heredity journal online. Selection of high MMR was effective in changing the mean MMR in all the four replicates at the genetic level.

However, none of the replicates showed a correlated genetic response of BMR to selection on MMR. Similarly, the antagonistic selection treatment leads to a marked response in MMR and a lack of response in BMR in all the replicate lines.

This general picture is consistent across the three types of analyses. In all the four control replicates, there was no genetic trend for either MMR or BMR. However, the joint analysis displays a small upward trend in MMR in the control lines. The Bayesian analysis was highly concordant with the results from the REML analysis see Supplementary Tables A2—A5.

In addition, Bayesian analysis displays estimates of dispersion parameters that vary considerably across replicates and lines. Combining the whole data set, the joint Bayesian analysis resulted in estimates of posterior means of genetic correlation r A between BMR and MMR equal to 0. Bayesian analysis also indicated that the common environmental variance attributable to natal cage that is, CAGE effect was quite small and did not explain significant variation for either metabolic trait.

MMR showed a clear response to selection in both the directionally selected and antagonistically selected lines. Pairwise comparisons with control lines show that BMR was not significantly higher in the high MMR lines, nor was it significantly lower in the antagonistically selected lines Table 2.

A priori one might have hypothesized an increase in BMR in the high selected lines and a decrease in BMR in the antagonistically selected lines, so the divergence in BMR would be predicted to be largest between those treatments.

That pairwise difference in BMR was significant even though neither of the selection treatments was significantly different in BMR from the control lines. The genetic analyses were largely concordant with the phenotypic analyses. Notably, phenotypic analyses during the early phase of selection produced mixed results, but Bayesian analysis offered clearer conclusions.

For example, estimates of genetic trend provided no statistically significant evidence of a correlated response in BMR in high-MMR mice Figure 1.

A plausible explanation for the lack of a correlated response in BMR in our lines selected for high MMR is inadequate statistical power. The relatively low heritabilities for BMR and MMR and the small genetic correlation between them would have required more generations before a statistically significant response was likely.

We suspect that further selection would have resulted in increased divergence in mass-adjusted BMR of high-MMR lines and ultimately to a statistically significant difference. Indeed, the expected correlated response in BMR can be approximated using infinite population theory with the formula:.

Falconer and Mackay where CR y is the correlated response in Y that is, BMR when selection is based on X that is, MMR , t is generations of selection, i is the selection intensity in X, h x and h y are the heritabilities, r A is the genetic correlation between X and Y and σ py is the phenotypic variance of Y.

A number of studies have suggested that BMR would likely show a correlated response to direct selection on MMR Koteja, ; Bozinovic, ; Dohm et al. Likewise many evolutionary models suggest complex interrelationships among traits linked to metabolic rates Ricklefs and Wikelski, ; Downs et al.

Although the selection for high MMR might possibly have led to high BMR in the evolutionary past, our results are equivocal with respect to the importance of a genetic covariance constraining the evolution of metabolic rates in the mice we studied.

This positive correlation is lower than an earlier report of a genetic correlation of 0. Previous studies of the genetic architecture of metabolic rates and theory both suggest that such sensitivity is not necessarily surprising, particularly in models that include numerous covariates and other statistical control factors Dohm et al.

Most importantly, both estimates suggest a positive correlation between MMR and BMR. Taken at face value, the genetic correlation is positive, as predicted by the aerobic capacity model , so that estimate per se does not falsify the aerobic capacity model Hayes, ; Nespolo and Roff, , but the weak genetic correlation and the lack of a correlated response to the selection in BMR suggest that the constraints imposed by genetic architecture are modest at most.

The results of another selection experiment for high MMR mice are concordant with our study. Gebczyński and Konarzewski a selected for high MMR during swimming.

While MMR increased as a result of selection they did not find a correlated increase in BMR. Hence, Gębczyński and Konarzewski concluded that there was no correlation and no mechanistic linkage between BMR and MMR.

In that study selection was on total distance run. Lines of mice selected to be high runners ran for more hours per day but not at faster speeds Garland et al.

Mice selected for higher voluntary running had elevated MMR during voluntary exercise Rezende et al. In addition, during forced exercise, neither MMR nor BMR was greater in mice selected for high voluntary running than in controls Rezende et al.

On the basis of these results, it would be intriguing to see what would happen to MMR and BMR if one selected on the intensity of exercise; that is, which would likely be more similar to our selection for high MMR. Such selection would presumably lead to increased MMR although it might also select for increased locomotor efficiency.

In contrast to studies which failed to find a link between MMR and BMR, work on bank voles supports the notion that selection on MMR can lead to changes in BMR.

In a population of bank voles Clethrionomys glareolus with a positive genetic correlation between BMR and MMR Sadowska et al. Genetic correlations can impose constraints on evolutionary trajectories, and these constraints can sometimes be absolute Walsh and Blows, Only a few studies have reported the genetic correlation between BMR and MMR for rodents.

The reported correlations range from 0. Even the highest of these values indicates that the genetic constraint between BMR and MMR is not absolute, and hence that some measure of independent evolution of each of the traits is possible Beldade et al. Our antagonistic selection treatment was designed to explore the effects of simultaneous selection for a combination of traits decreased BMR and increased MMR.

Clearly in the absence of absolute genetic constraints that is, the genetic variances do not equal zero and the genetic correlation does not equal 1 independent evolution of BMR and MMR is possible. On average, the difference in BMR between antag-MMR and control mice was 4. The response to selection in our antag-MR mice might indicate that the physiological design of vertebrates does not preclude animals having an increased MMR and simultaneously having a decreased BMR at least within certain limits.

It would be interesting to learn how far those limits could be extended that is, how low selection could move BMR while simultaneously increasing MMR or at least keeping MMR from decreasing. This idea is consistent with the work on bank voles showing that MMR and BMR are under different selective pressures Boratynski and Koteja, One caveat about our antagonistic selection treatment is that these mice were maintained in a benign laboratory setting that included relatively warm temperatures and ad libitum food.

It is possible that our mice selected for decreased BMR and increased MMR might have attributes, which preclude success in a natural environment.

In other words, decreased BMR and increased MMR might be achievable in the laboratory but not in nature. However, limited data suggest that some bats and canids have high factorial aerobic scopes ratio of MMR to BMR in nature Koteja, , so the notion of evolving high MMR accompanied by low BMR is plausible.

To summarize, selection for increased MMR led to clear positive responses in MMR, but without a correlated response in BMR. The small and positive genetic correlation between BMR and MMR did not falsify the aerobic capacity model for the evolution of endothermy, but the low genetic correlation suggests that constraints on independent evolution are modest at most.

Moreover, the precise estimates of the genetic correlation proved sensitive to sampling for example, Wone et al. Interestingly, in antagonistically selected mice, it was possible to simultaneously achieve increased MMR and decreased BMR.

Collectively, our results are in concert with those of others for example, Sadowska et al. Nonetheless, in the mice we studied, the genetic correlation is low enough to not preclude the substantial independent evolution of metabolic traits Pease and Bull, ; Walsh and Blows, Angilletta MJ, Sears MW.

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Ann Rev Physiol 57 : 69— Currently available quantitative genetic data are not yet sufficient to test metabolic scaling theory in a rigorous way. Although the meta-analytical weighted average b A of 0. They are also insufficient to test theory that predicts diverse metabolic scaling exponents, such as the metabolic-level boundaries hypothesis MLBH: Glazier, , b.

However, it is intriguing that, as predicted by the MLBH, the birds and mammals with relatively high basal metabolic rates BMRs [ Saxicola torquata , Taeniopygia guttata , Myodes glareolus and Mustela nivalis , with mean BMR values of musculus and Peromyscus maniculatus , with b A values of 0.

The MLBH predicts that the scaling exponent for resting or basal metabolic rate RMR or BMR should decrease as overall metabolic level L increases Glazier, , b. Nevertheless, the negative relationship between b A and L observed here is based on only six species with b A values that have large error terms, and therefore is unsurprisingly not statistically significant.

The MLBH also predicts that the scaling exponent for active maximal metabolic rate MMR should be higher than that for RMR, which, however, is not seen for either b P or b A in the house mouse M.

musculus 0. However, activity has been shown to increase b P within other animal species, e. the laboratory rat Refinetti, , humans Rogers et al. More data are obviously needed. Available data on V A given in Table 3 do permit a useful test of two different theoretical approaches regarding the evolution of metabolic scaling that make opposite predictions.

The first theory posits that metabolic scaling is chiefly the result of evolutionary optimization of body size with secondary effects on MR Kozłowski and Weiner, ; Kozłowski et al.

According to this theory, as applied by Ketola and Kotiaho , if natural selection acts more intensely on body size than MR, then V A and h 2 should be lower for body size than that for MR. The second approach, based on dynamic energy budget theory, proposes that evolutionary optimization is focused primarily on MR with secondary effects on body size Lika et al.

If correct, then V A and h 2 should be lower for MR than body size. However, comparisons of h 2 and V A estimates from our models Table 3 , Fig. Therefore, although natural selection may act on MR and BM to varying degrees in different environments to cause evolutionary changes in metabolic scaling see also Witting, , existing data suggest that there is no general difference in the intensity of selection on — and evolutionary potential in — either trait.

In agreement, Videlier et al. However, Boratyński and Koteja found that BMR, but not BM, was related to relative fitness reproductive success in the bank vole M. More studies of this kind are needed, as well as those that specifically test for effects of selection on the covariance of MR and BM, and thereby metabolic scaling.

In some investigations, the metabolic scaling exponent b is considered to be a trait in and of itself. For example, Fossen et al. Although it may be useful to consider b as a trait when working with genetically identical clones or strains e. Glazier and Calow, ; Yashchenko et al. Moreover, the two quantitative genetic studies conducted so far on b considered as a trait found no detectable V A in b Fossen et al.

Accordingly, genes and natural selection affect b only indirectly via their direct effects on the individual traits of MR and BM, the covariances of which determine metabolic scaling. Otherwise, considering b as a trait makes it problematic to study metabolic scaling from a genetically based adaptive perspective, given that b represents the covariance of two complex traits MR and BM subject to multiple environmental and genetic effects and variable selection regimes Arnold et al.

In this study, we hope to have shown the value of quantitative genetic studies in increasing our understanding of the evolution of metabolic scaling.

Other useful methods have been proposed to examine genetic effects on metabolic scaling, including quantitative trait locus analyses Wu et al. In addition, genetic effects on metabolic scaling can be studied through the comparison of metabolic scaling relationships observed in genetically different clones, strains or populations Glazier and Calow, ; Glazier et al.

We encourage researchers interested in the evolution of metabolic scaling to adopt a quantitative genetics approach that examines how b A may be influenced by evolutionary processes acting on BM and MR, in accordance with how evolutionary biologists have traditionally evaluated changes in G and genetic correlations under artificial or natural selection e.

Careau et al. We thank all authors from the original studies for conducting all of the work to collect the data and for sharing the datasets, and Wolfgang Forstmeier for guidance on the meta-analytical approach.

Conceptualization: V. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Skip Nav Destination Close navigation menu Article navigation. Previous Article Next Article. Article contents. MATERIALS AND METHODS. Article Navigation. RESEARCH ARTICLE 08 March A quantitative genetics perspective on the body-mass scaling of metabolic rate Vincent Careau This site. Google Scholar.

Douglas S. Glazier Author and article information. Competing interests The authors declare no competing or financial interests. Received: 07 Sep Accepted: 13 Jan Online ISSN: Published by The Company of Biologists Ltd.

Article history Received:. Cite Icon Cite. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. How fast an organism carries out various vital functions is of fundamental importance to its ecological and evolutionary success.

However, the rates of an organism's activities may be relatively slow or fast, for reasons that are incompletely understood. Because all biological processes require metabolic energy, their rates are often paralleled by the rate of metabolism, which constitutes all of the biochemical reactions by which energy and materials are transformed for various activities and structures.

One of the most important intrinsic factors affecting MR is body size. Relationships between MR and body mass BM among individuals, populations or species are typically so strong and regular that they can often be well described by simple power or log-linear functions. This is particularly the case in:.

Table 1. Datasets included in this study. View Large. View large Download slide. Table 2. Table 3. In our sample of 11 paired estimates, b A and b P were significantly correlated Fig. There are many reasons why this assumption should be avoided, just as a phenotypic correlation r P estimate is generally not an accurate indicator of r A Kruuk et al.

The correspondence between b A and b P depends on the relative amount of co variance for the genetic versus other components of the total phenotypic covariance of MR and BM. In a simplified example in which the only sources of co variance between BM and MR are genetic and residual i.

no sources of common environment variance , then:. Author contributions Conceptualization: V. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Search ADS. Quantification of correlational selection on thermal physiology, thermoregulatory behavior, and energy metabolism in lizards.

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Genetic variation for ontogenetic shifts in metabolism underlies physiological homeostasis in Drosophila. Heritability of flight and resting metabolic rates in the Glanville fritillary butterfly. Quantitative Genetics in the Wild Oxford.

The quantitative genetics of physiological and morphological traits in an invasive terrestrial snail: additive vs. non—additive genetic variation.

Metabolic scaling of individuals vs. populations: Evidence for variation in scaling exponents at different hierarchical levels. Scaling for the VO 2 -to-body size relationship among children and adults. Basal metabolic rate: heritability and genetic correlations with morphological traits in the zebra finch.

Genetic correlations between basal and maximum metabolic rates in a wild rodent: consequences for evolution of endothermy.

Evolution of basal metabolic rate in bank voles from a multidirectional selection experiment. Application des sciences accessoires et principalement des mathématiques à la physiologie générale. Discontinuous gas exchange exhibition is a heritable trait in speckled cockroaches Nauphoeta cinerea.

Concerted evolution of body mass, cell size and metabolic rate among carabid beetles. Quantitative genetics parameters show partial independent evolutionary potential for body mass and metabolism in stonechats from different populations. Van Voorhies.

Lack of correlation between body mass and metabolic rate in Drosophila melanogaster. A common genetic basis to the origin of the leaf economics spectrum and metabolic scaling allometry. Quantifying selection on standard metabolic rate and body mass in Drosophila melanogaster.

Sex—specific genetic co variances of standard metabolic rate, body mass and locomotor activity in Drosophila melanogaster. von Hoesslin. Über die Ursache der scheinbaren Abhängigkeit des Umsatzes von der Grösse der Körperoberfläche.

Metabolic scaling in animals: methods, empirical results, and theoretical explanations. The natural selection of metabolism and mass selects allometric transitions from prokaryotes to mammals. Genetic variances and covariances of aerobic metabolic rates in laboratory mice. A statistical model for the genetic origin of allometric scaling laws in biology.

Negative relationships between population density and metabolic rates are not general. Genetic association analysis for relative growths of body compositions and metabolic traits to body weights in broilers. Environmental and genetic influences on body mass and resting metabolic rates RMR in a natural population of weasel Mustela nivalis.

Supplementary information Supplementary information - pdf file. Email alerts Article activity alert. Accepted manuscripts alert. Table of contents alert. Latest published articles alert. View Metrics. Cited by Web of Science 3.

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Thank you for visiting gfnetics. Body recomposition tips are using Self-care for anxiety browser Metsbolic with limited ratr Self-care for anxiety CSS. To obtain the best experience, we recommend you use a Onion soup variations up to date browser or turn off Mehabolic mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Metabolic rates are correlated with many aspects of ecology, but how selection on different aspects of metabolic rates affects their mutual evolution is poorly understood. Using laboratory mice, we artificially selected for high maximal mass-independent metabolic rate MMR without direct selection on mass-independent basal metabolic rate BMR. Then we tested for responses to selection in MMR and correlated responses to selection in BMR.